Instructions to use AmelieSchreiber/esm_interact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmelieSchreiber/esm_interact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AmelieSchreiber/esm_interact")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AmelieSchreiber/esm_interact") model = AutoModelForMaskedLM.from_pretrained("AmelieSchreiber/esm_interact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c790adf9427c65478c6cdb7c6a71789ccac4cebd8d4bcdc214a6002e8a5b591e
- Size of remote file:
- 1.19 GB
- SHA256:
- b1c63c3a0acd7d18cfeddd162b0971f645acfd9efdbaf2c572757e31aa65b860
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.